Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations567923
Missing cells0
Missing cells (%)0.0%
Duplicate rows5194
Duplicate rows (%)0.9%
Total size in memory94.1 MiB
Average record size in memory173.8 B

Variable types

Categorical9
Numeric8

Alerts

Dataset has 5194 (0.9%) duplicate rowsDuplicates
COLE_MCPIO_UBICACION is highly overall correlated with ESTU_MCPIO_RESIDEHigh correlation
ESTU_MCPIO_RESIDE is highly overall correlated with COLE_MCPIO_UBICACIONHigh correlation
FAMI_EDUCACIONMADRE is highly overall correlated with FAMI_TIENEAUTOMOVIL and 1 other fieldsHigh correlation
FAMI_TIENEAUTOMOVIL is highly overall correlated with FAMI_EDUCACIONMADRE and 1 other fieldsHigh correlation
FAMI_TIENECOMPUTADOR is highly overall correlated with FAMI_EDUCACIONMADRE and 1 other fieldsHigh correlation
COLE_BILINGUE is highly imbalanced (58.6%) Imbalance
COLE_GENERO is highly imbalanced (83.2%) Imbalance
COLE_SEDE_PRINCIPAL is highly imbalanced (85.4%) Imbalance
FAMI_TIENEAUTOMOVIL is highly imbalanced (51.5%) Imbalance
FAMI_PERSONASHOGAR has 133821 (23.6%) zeros Zeros

Reproduction

Analysis started2025-05-25 20:23:07.915688
Analysis finished2025-05-25 20:23:18.316662
Duration10.4 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
1
488403 
0
79520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Length

2025-05-25T15:23:18.378074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:18.422548image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Most occurring characters

ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 488403
86.0%
0 79520
 
14.0%

COLE_BILINGUE
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
0
488851 
2
69544 
1
 
9528

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Length

2025-05-25T15:23:18.463534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:18.501089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 488851
86.1%
2 69544
 
12.2%
1 9528
 
1.7%

COLE_CARACTER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
0
313048 
3
189128 
2
60231 
4
 
3636
1
 
1880

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Length

2025-05-25T15:23:18.543370image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:18.582373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 313048
55.1%
3 189128
33.3%
2 60231
 
10.6%
4 3636
 
0.6%
1 1880
 
0.3%

COLE_GENERO
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
2
546025 
0
 
16874
1
 
5024

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Length

2025-05-25T15:23:18.628686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:18.665467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 546025
96.1%
0 16874
 
3.0%
1 5024
 
0.9%

COLE_MCPIO_UBICACION
Real number (ℝ)

High correlation 

Distinct1026
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412.08957
Minimum0
Maximum1025
Zeros169
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-05-25T15:23:18.713247image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile83
Q1100
median352
Q3642
95-th percentile972
Maximum1025
Range1025
Interquartile range (IQR)542

Descriptive statistics

Standard deviation307.37883
Coefficient of variation (CV)0.74590297
Kurtosis-1.1372441
Mean412.08957
Median Absolute Deviation (MAD)252
Skewness0.46758775
Sum2.3403514 × 108
Variance94481.748
MonotonicityNot monotonic
2025-05-25T15:23:18.771597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 96691
 
17.0%
512 31127
 
5.5%
132 24427
 
4.3%
83 17104
 
3.0%
156 13145
 
2.3%
240 8864
 
1.6%
393 7774
 
1.4%
107 7028
 
1.2%
999 6740
 
1.2%
855 6701
 
1.2%
Other values (1016) 348322
61.3%
ValueCountFrequency (%)
0 169
 
< 0.1%
1 260
 
< 0.1%
2 29
 
< 0.1%
3 1021
0.2%
4 78
 
< 0.1%
5 378
 
0.1%
6 319
 
0.1%
7 115
 
< 0.1%
8 131
 
< 0.1%
9 1158
0.2%
ValueCountFrequency (%)
1025 774
0.1%
1024 1723
0.3%
1023 64
 
< 0.1%
1022 77
 
< 0.1%
1021 407
 
0.1%
1020 294
 
0.1%
1019 88
 
< 0.1%
1018 140
 
< 0.1%
1017 165
 
< 0.1%
1016 1414
0.2%

COLE_NATURALEZA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
1
405598 
0
162325 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Length

2025-05-25T15:23:18.826162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:18.863068image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Most occurring characters

ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 405598
71.4%
0 162325
28.6%

COLE_SEDE_PRINCIPAL
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
9
556147 
8
 
11776

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Length

2025-05-25T15:23:18.904545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:18.941299image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Most occurring characters

ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 556147
97.9%
8 11776
 
2.1%

ESTU_GENERO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
335
309076 
336
256419 
337
 
2428

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1703769
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row336
2nd row336
3rd row336
4th row336
5th row335

Common Values

ValueCountFrequency (%)
335 309076
54.4%
336 256419
45.2%
337 2428
 
0.4%

Length

2025-05-25T15:23:18.982582image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:19.020841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
335 309076
54.4%
336 256419
45.2%
337 2428
 
0.4%

Most occurring characters

ValueCountFrequency (%)
3 1135846
66.7%
5 309076
 
18.1%
6 256419
 
15.1%
7 2428
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1703769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1135846
66.7%
5 309076
 
18.1%
6 256419
 
15.1%
7 2428
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1703769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1135846
66.7%
5 309076
 
18.1%
6 256419
 
15.1%
7 2428
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1703769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1135846
66.7%
5 309076
 
18.1%
6 256419
 
15.1%
7 2428
 
0.1%

ESTU_MCPIO_RESIDE
Real number (ℝ)

High correlation 

Distinct1031
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.45271
Minimum0
Maximum1033
Zeros205
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-05-25T15:23:19.070226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile82
Q199
median354
Q3643
95-th percentile976
Maximum1033
Range1033
Interquartile range (IQR)544

Descriptive statistics

Standard deviation307.74001
Coefficient of variation (CV)0.74793532
Kurtosis-1.1325442
Mean411.45271
Median Absolute Deviation (MAD)255
Skewness0.46460955
Sum2.3367346 × 108
Variance94703.914
MonotonicityNot monotonic
2025-05-25T15:23:19.127885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 97830
 
17.2%
515 30746
 
5.4%
128 24300
 
4.3%
82 17221
 
3.0%
149 13193
 
2.3%
249 8845
 
1.6%
400 7842
 
1.4%
859 6781
 
1.2%
106 6745
 
1.2%
1001 6656
 
1.2%
Other values (1021) 347764
61.2%
ValueCountFrequency (%)
0 205
 
< 0.1%
1 32
 
< 0.1%
2 1032
0.2%
3 79
 
< 0.1%
4 232
 
< 0.1%
5 294
 
0.1%
6 106
 
< 0.1%
7 126
 
< 0.1%
8 1148
0.2%
9 24
 
< 0.1%
ValueCountFrequency (%)
1033 53
 
< 0.1%
1032 93
 
< 0.1%
1031 125
 
< 0.1%
1030 239
 
< 0.1%
1028 778
0.1%
1027 1783
0.3%
1026 67
 
< 0.1%
1025 74
 
< 0.1%
1024 440
 
0.1%
1023 300
 
0.1%

FAMI_CUARTOSHOGAR
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.495907
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-05-25T15:23:19.174057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median4
Q310
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4550225
Coefficient of variation (CV)0.53187683
Kurtosis-1.7384593
Mean6.495907
Median Absolute Deviation (MAD)3
Skewness-0.035496842
Sum3689175
Variance11.93718
MonotonicityNot monotonic
2025-05-25T15:23:19.215861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 238122
41.9%
4 202961
35.7%
2 73540
 
12.9%
11 20873
 
3.7%
1 20243
 
3.6%
8 6573
 
1.2%
9 2307
 
0.4%
7 1238
 
0.2%
12 1088
 
0.2%
3 584
 
0.1%
ValueCountFrequency (%)
1 20243
 
3.6%
2 73540
 
12.9%
3 584
 
0.1%
4 202961
35.7%
6 394
 
0.1%
7 1238
 
0.2%
8 6573
 
1.2%
9 2307
 
0.4%
10 238122
41.9%
11 20873
 
3.7%
ValueCountFrequency (%)
12 1088
 
0.2%
11 20873
 
3.7%
10 238122
41.9%
9 2307
 
0.4%
8 6573
 
1.2%
7 1238
 
0.2%
6 394
 
0.1%
4 202961
35.7%
3 584
 
0.1%
2 73540
 
12.9%

FAMI_EDUCACIONMADRE
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.999986
Minimum4
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-05-25T15:23:19.255550image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q111
median13
Q313
95-th percentile18
Maximum21
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4557412
Coefficient of variation (CV)0.28797877
Kurtosis0.98108463
Mean11.999986
Median Absolute Deviation (MAD)1
Skewness-0.68899166
Sum6815068
Variance11.942147
MonotonicityNot monotonic
2025-05-25T15:23:19.299770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
13 158986
28.0%
11 94109
16.6%
14 86906
15.3%
12 84650
14.9%
4 50070
 
8.8%
18 40561
 
7.1%
7 12491
 
2.2%
10 11590
 
2.0%
19 11539
 
2.0%
8 8366
 
1.5%
Other values (2) 8655
 
1.5%
ValueCountFrequency (%)
4 50070
 
8.8%
5 7567
 
1.3%
7 12491
 
2.2%
8 8366
 
1.5%
10 11590
 
2.0%
11 94109
16.6%
12 84650
14.9%
13 158986
28.0%
14 86906
15.3%
18 40561
 
7.1%
ValueCountFrequency (%)
21 1088
 
0.2%
19 11539
 
2.0%
18 40561
 
7.1%
14 86906
15.3%
13 158986
28.0%
12 84650
14.9%
11 94109
16.6%
10 11590
 
2.0%
8 8366
 
1.5%
7 12491
 
2.2%

FAMI_EDUCACIONPADRE
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8332644
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-05-25T15:23:19.343238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q17
median8
Q39
95-th percentile13
Maximum15
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7363163
Coefficient of variation (CV)0.34932004
Kurtosis0.36608831
Mean7.8332644
Median Absolute Deviation (MAD)1
Skewness-0.36345494
Sum4448691
Variance7.4874266
MonotonicityNot monotonic
2025-05-25T15:23:19.390119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 139042
24.5%
8 99593
17.5%
7 96640
17.0%
10 76085
13.4%
2 47395
 
8.3%
13 31145
 
5.5%
4 25936
 
4.6%
5 22217
 
3.9%
6 12111
 
2.1%
14 9976
 
1.8%
Other values (2) 7783
 
1.4%
ValueCountFrequency (%)
2 47395
 
8.3%
3 6695
 
1.2%
4 25936
 
4.6%
5 22217
 
3.9%
6 12111
 
2.1%
7 96640
17.0%
8 99593
17.5%
9 139042
24.5%
10 76085
13.4%
13 31145
 
5.5%
ValueCountFrequency (%)
15 1088
 
0.2%
14 9976
 
1.8%
13 31145
 
5.5%
10 76085
13.4%
9 139042
24.5%
8 99593
17.5%
7 96640
17.0%
6 12111
 
2.1%
5 22217
 
3.9%
4 25936
 
4.6%

FAMI_ESTRATOVIVIENDA
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8915557
Minimum2
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-05-25T15:23:19.508417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q33
95-th percentile5
Maximum17
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0210741
Coefficient of variation (CV)0.35312275
Kurtosis9.9852352
Mean2.8915557
Median Absolute Deviation (MAD)1
Skewness1.8110993
Sum1642181
Variance1.0425924
MonotonicityNot monotonic
2025-05-25T15:23:19.549070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 244474
43.0%
3 193719
34.1%
4 95694
 
16.8%
5 21150
 
3.7%
6 7962
 
1.4%
7 4793
 
0.8%
17 131
 
< 0.1%
ValueCountFrequency (%)
2 244474
43.0%
3 193719
34.1%
4 95694
 
16.8%
5 21150
 
3.7%
6 7962
 
1.4%
7 4793
 
0.8%
17 131
 
< 0.1%
ValueCountFrequency (%)
17 131
 
< 0.1%
7 4793
 
0.8%
6 7962
 
1.4%
5 21150
 
3.7%
4 95694
 
16.8%
3 193719
34.1%
2 244474
43.0%

FAMI_PERSONASHOGAR
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1706587
Minimum0
Maximum24
Zeros133821
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-05-25T15:23:19.591330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q319
95-th percentile21
Maximum24
Range24
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.2300168
Coefficient of variation (CV)1.1296539
Kurtosis-1.6643189
Mean8.1706587
Median Absolute Deviation (MAD)1
Skewness0.49215393
Sum4640305
Variance85.193211
MonotonicityNot monotonic
2025-05-25T15:23:19.634622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 173763
30.6%
0 133821
23.6%
21 100417
17.7%
19 63658
 
11.2%
4 30132
 
5.3%
20 29556
 
5.2%
13 15452
 
2.7%
12 6507
 
1.1%
2 4480
 
0.8%
23 3653
 
0.6%
Other values (3) 6484
 
1.1%
ValueCountFrequency (%)
0 133821
23.6%
1 173763
30.6%
2 4480
 
0.8%
3 3360
 
0.6%
4 30132
 
5.3%
12 6507
 
1.1%
13 15452
 
2.7%
14 2036
 
0.4%
19 63658
 
11.2%
20 29556
 
5.2%
ValueCountFrequency (%)
24 1088
 
0.2%
23 3653
 
0.6%
21 100417
17.7%
20 29556
 
5.2%
19 63658
11.2%
14 2036
 
0.4%
13 15452
 
2.7%
12 6507
 
1.1%
4 30132
 
5.3%
3 3360
 
0.6%

FAMI_TIENEAUTOMOVIL
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
8
445086 
13
121749 
16
 
1088

Length

Max length2
Median length1
Mean length1.2162916
Min length1

Characters and Unicode

Total characters690760
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 445086
78.4%
13 121749
 
21.4%
16 1088
 
0.2%

Length

2025-05-25T15:23:19.679070image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:19.716586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
8 445086
78.4%
13 121749
 
21.4%
16 1088
 
0.2%

Most occurring characters

ValueCountFrequency (%)
8 445086
64.4%
1 122837
 
17.8%
3 121749
 
17.6%
6 1088
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 690760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 445086
64.4%
1 122837
 
17.8%
3 121749
 
17.6%
6 1088
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 690760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 445086
64.4%
1 122837
 
17.8%
3 121749
 
17.6%
6 1088
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 690760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 445086
64.4%
1 122837
 
17.8%
3 121749
 
17.6%
6 1088
 
0.2%

FAMI_TIENECOMPUTADOR
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 MiB
5
349678 
3
217157 
7
 
1088

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters567923
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row5
5th row3

Common Values

ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Length

2025-05-25T15:23:19.758101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:23:19.795400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Most occurring characters

ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 349678
61.6%
3 217157
38.2%
7 1088
 
0.2%

PUNT_GLOBAL
Real number (ℝ)

Distinct456
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.98529
Minimum0
Maximum492
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size24.8 MiB
2025-05-25T15:23:19.840038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile182
Q1216
median246
Q3280
95-th percentile340
Maximum492
Range492
Interquartile range (IQR)64

Descriptive statistics

Standard deviation48.496796
Coefficient of variation (CV)0.19322565
Kurtosis0.61171879
Mean250.98529
Median Absolute Deviation (MAD)31
Skewness0.63938216
Sum1.4254032 × 108
Variance2351.9392
MonotonicityNot monotonic
2025-05-25T15:23:19.894917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 5791
 
1.0%
232 5759
 
1.0%
242 5725
 
1.0%
243 5674
 
1.0%
245 5659
 
1.0%
235 5643
 
1.0%
237 5625
 
1.0%
228 5615
 
1.0%
248 5588
 
1.0%
238 5582
 
1.0%
Other values (446) 511262
90.0%
ValueCountFrequency (%)
0 4
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 2
< 0.1%
12 2
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
492 1
 
< 0.1%
490 1
 
< 0.1%
488 1
 
< 0.1%
483 2
< 0.1%
479 1
 
< 0.1%
478 2
< 0.1%
477 3
< 0.1%
476 4
< 0.1%
475 1
 
< 0.1%
473 2
< 0.1%

Interactions

2025-05-25T15:23:16.638371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.000925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.531950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.029263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.551258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.085530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.576304image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.108465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.707646image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.076804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.595637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.094063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.618386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.144722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.637264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.172813image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.779339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.140281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.652443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.162508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.689568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.203939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.702713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.234266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.848118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.202629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.710388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.220486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.753489image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.260629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.763342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.297336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.933555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.271274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.776190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.285152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.820911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.319990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.827137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.373511image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:17.014181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.336568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.841826image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.353739image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.884855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.376602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.892921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.436741image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:17.099488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.406361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.906959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.421002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.957586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.442405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.965473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.503897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:17.167439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.467285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:13.965003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:14.482706image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.020310image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:15.505014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.034030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-25T15:23:16.563361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-25T15:23:19.939937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
COLE_AREA_UBICACIONCOLE_BILINGUECOLE_CARACTERCOLE_GENEROCOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_SEDE_PRINCIPALESTU_GENEROESTU_MCPIO_RESIDEFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORPUNT_GLOBAL
COLE_AREA_UBICACION1.0000.1370.1200.0660.1980.1630.0550.0200.1860.0530.2240.2310.1520.0570.0880.2430.135
COLE_BILINGUE0.1371.0000.1350.0470.0450.1230.0040.0090.0420.0200.1110.0710.2100.0130.0700.0320.130
COLE_CARACTER0.1200.1351.0000.0350.1320.3840.0560.0260.1330.0140.0710.0600.0920.0180.0880.0650.058
COLE_GENERO0.0660.0470.0351.0000.0710.1160.0290.1180.0670.0300.1380.1080.1410.0230.0960.0760.131
COLE_MCPIO_UBICACION0.1980.0450.1320.0711.0000.1930.0610.0140.939-0.004-0.026-0.016-0.2070.0020.0910.152-0.107
COLE_NATURALEZA0.1630.1230.3840.1160.1931.0000.0910.0450.2020.1130.3550.3020.3950.1000.3080.2470.248
COLE_SEDE_PRINCIPAL0.0550.0040.0560.0290.0610.0911.0000.0080.0580.0130.0530.0500.0440.0130.0300.0370.039
ESTU_GENERO0.0200.0090.0260.1180.0140.0450.0081.0000.0140.0310.0370.0340.0260.0130.0260.0200.066
ESTU_MCPIO_RESIDE0.1860.0420.1330.0670.9390.2020.0580.0141.000-0.003-0.017-0.009-0.2180.0030.1010.158-0.112
FAMI_CUARTOSHOGAR0.0530.0200.0140.030-0.0040.1130.0130.031-0.0031.0000.0080.0080.014-0.0120.1950.2080.013
FAMI_EDUCACIONMADRE0.2240.1110.0710.138-0.0260.3550.0530.037-0.0170.0081.0000.385-0.022-0.0200.7590.759-0.014
FAMI_EDUCACIONPADRE0.2310.0710.0600.108-0.0160.3020.0500.034-0.0090.0080.3851.000-0.037-0.0280.3130.330-0.029
FAMI_ESTRATOVIVIENDA0.1520.2100.0920.141-0.2070.3950.0440.026-0.2180.014-0.022-0.0371.0000.0050.3890.3420.394
FAMI_PERSONASHOGAR0.0570.0130.0180.0230.0020.1000.0130.0130.003-0.012-0.020-0.0280.0051.0000.3420.345-0.012
FAMI_TIENEAUTOMOVIL0.0880.0700.0880.0960.0910.3080.0300.0260.1010.1950.7590.3130.3890.3421.0000.7420.209
FAMI_TIENECOMPUTADOR0.2430.0320.0650.0760.1520.2470.0370.0200.1580.2080.7590.3300.3420.3450.7421.0000.216
PUNT_GLOBAL0.1350.1300.0580.131-0.1070.2480.0390.066-0.1120.013-0.014-0.0290.394-0.0120.2090.2161.000

Missing values

2025-05-25T15:23:17.241677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-25T15:23:17.708849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

COLE_AREA_UBICACIONCOLE_BILINGUECOLE_CARACTERCOLE_GENEROCOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_SEDE_PRINCIPALESTU_GENEROESTU_MCPIO_RESIDEFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORPUNT_GLOBAL
0100242219336428211732083247
1103210019336994141031985309
20202400193361031212921385206
31202100093369921372085258
4000248519335490212922083232
511025251933552941822185257
6103051219335851013143185261
71002783193357851012832185199
80032100193359941172183240
9103262019335621218540135297
COLE_AREA_UBICACIONCOLE_BILINGUECOLE_CARACTERCOLE_GENEROCOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_SEDE_PRINCIPALESTU_GENEROESTU_MCPIO_RESIDEFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORPUNT_GLOBAL
570454000272619336104211102083268
57045510223141933531910111022185221
570456100255609335560101393185216
570457020229519336298101282083291
57045810321016193351019101392183240
57045910025371933554241392085259
570460100251219335515111153485260
570461103213193351241272183209
57046210329961933599811141021983262
5704630022308193353141111832183297

Duplicate rows

Most frequently occurring

COLE_AREA_UBICACIONCOLE_BILINGUECOLE_CARACTERCOLE_GENEROCOLE_MCPIO_UBICACIONCOLE_NATURALEZACOLE_SEDE_PRINCIPALESTU_GENEROESTU_MCPIO_RESIDEFAMI_CUARTOSHOGARFAMI_EDUCACIONMADREFAMI_EDUCACIONPADREFAMI_ESTRATOVIVIENDAFAMI_PERSONASHOGARFAMI_TIENEAUTOMOVILFAMI_TIENECOMPUTADORPUNT_GLOBAL# duplicates
1347100210009336991042411352906
1433100210009336991042511353386
2145100210019335994139321852436
647100210009335991042411352685
652100210009335991042411352825
665100210009335991042411353205
669100210009335991042411353295
709100210009335991042511352925
722100210009335991042511353435
944100210009335991013941852445